Surrogate driven respiratory motion model derived from CBCT projection data

Cone Beam Computed Tomography (CBCT) is the most common imaging method for Image Guided Radiation Therapy (IGRT). However due to the slow rotating gantry, the image quality of CBCT can be adversely affected by respiratory motion, as it blurs the tumour and nearby organs at risk (OARs), which makes visualization of organ boundaries difficult, in particular for organs in the thoracic region. Currently one approach to tackle the problem of respiratory motion is the use of respiratory motion model to compensate for the motion during CBCT image reconstruction. The overall goal of this work is to estimate the 3D motion, including the breath-to-breath variability, on the day of treatment directly from the CBCT projection data, without requiring any external devices. The work presented here consist of two main parts: firstly, we introduce a novel data driven method based on Principal Component Analysis PCA, with the goal to extract a surrogate signal related to the internal anatomy from the CBCT projections. Secondly, using the extracted signals, we use surrogate-driven respiratory motion models to estimate the patient’s 3D respiratory motion. We utilized a recently developed generalized framework that unifies image registration and correspondence model fitting into a single optimization. This enables the model to be fitted directly to unsorted/unreconstructed data (CBCT projection data), thereby allowing an estimate of the patient’s respiratory motion on the day of treatment. To evaluate our methods, we have used an anthropomorphic software phantom combined with CBCT projection simulations. We have also tested the proposed method on clinical data with promising results obtained

Optimization of Radiation Therapy in Time-Dependent Anatomy

The objective of this dissertation is to develop treatment planning techniques that have the potential to improve radiation therapy of time-dependent (4D) anatomy. Specifically, this study examines dose estimation, dose evaluation, and decision making in the context of optimizing lung cancer radiation therapy. Two methods of dose estimation are compared in patients with locally advanced and early stage lung cancer: dose computed on a single image (3D-dose) and deformably registered, accumulated dose (or 4D-dose). The results indicate that differences between 3D- and 4D- dose are not significant in organs at risk (OARs), however, 4D-dose to a moving lung cancer target can deviate from 3D-dose. These differences imply that optimization of the 4D-dose through multiple-anatomy optimization (MAO) can improve radiation therapy in 4D-anatomy. MAO incorporates time-dependent target and OAR geometry while enabling a simple, clinically realizable delivery. MAO has the potential to enhance the therapeutic ratio in terms of target coverage and OAR sparing in 4D-anatomy. In dose evaluation within 4D-anatomy; dose-to-mass is a more intuitive and precise metric in estimating the effects of radiation in tissues. Assuming physical density is proportional to functional tissue density, dose-to-mass has a 1-1 correspondence with radiation damage. Dose-to-mass optimization boosts dose in massive regions of lung cancer targets and can reduce integral dose to lung by preferentially treating through regions of low-density lung tissue. Finally, multi-criteria optimization (MCO) is implemented in order to clarify decision making during plan design for lung cancer treatment. An MCO basis set establishes a patient-specific decision space which reveals trade-offs in OAR-dose at a fixed, constrained target dose. By interpolating the MCO basis set and evaluating the plan on 4D-anatomy, patient- and organ- specific conservatism in plan design can be expressed in real time. Through improved methods of dose estimation, dose evaluation, and decision making, this dissertation will positively impact radiation therapy of time-dependent anatomy

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Robustness of photon dose distributions against intra and inter-fraction anatomical changes for whole lung irradiation

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia ) Universidade de Lisboa, Faculdade de Ciências, 2022Approximately 400 000 children are diagnosed with cancer every year, with the most common types of childhood cancers being leukemia, lymphoma, brain and solid tumors. Moreover, 10-40% of children with solid tumors present lung metastases at the time of diagnosis. Whole lung irradiation (WLI) is a treatment option for pediatric patients with lung metastases that develop from solid tumors like Ewing’s sarcoma (ES), rhabdomyosarcoma, and Wilms’ tumor (WT). However, during treatment delivery, intra and inter-fraction anatomical changes might occur and can greatly affect treatment outcome. These changes can either cause underdosages of the target volume, or overdosage of surrounding organs at risk (OARs). Since growing lung tissue is more sensitive to radiation, avoiding the irradiation of OARs while maintaining target coverage is of utmost importance. Thus, there is the need to ensure that a treatment plan is robust, which is accomplished if the planned and delivery dose distributions agree even in the presence of uncertainties. To evaluate how anatomical changes affect the treatment outcome when delivering WLI to pediatric patients, this thesis comprises a study of robustness of photon dose distributions against intra and inter-fraction anatomical changes. The present study includes treatment plans of 21 pediatric patients that received WLI at University Medical Center Utrecht. The robustness evaluation was performed against intra and inter-fraction anatomical changes. Intra-fraction changes were evaluated by recalculating the original plan on the two extreme breathing phases – maximum inhalation and maximum exhalation. Conversely, inter-fraction changes were evaluated by calculating the fractional dose on the daily cone-beam computed tomography (CBCT) images acquired before treatment and accumulating the resulting dose distributions. The recalculated plans were then compared to the original dose distribution. Overall results of the study demonstrated no clinically relevant differences in terms of mean internal target volume (ITV) coverage of the lungs. However, hot spot values differed significantly for three patients. The differences observed for the V107% were due to diaphragm position shifts (two patients) and electron density (ED) changes within the lung ITV (one patient). Coverage and hot spots of metastases presented clinically relevant differences when considering the extreme breathing phases, as well as the registered-CTs. This was due to differences in ED within the metastases and position variations in relation to surrounding structures (like the heart or ribs). However, these changes were observed for the PTV only, while ITV coverage remained around 100% on all plans. OAR dose values were robust against intra and inter-fraction anatomical changes, with no clinically relevant differences to report. In conclusion, the recalculated WLI plans are considered robust against intra and inter-fraction anatomical changes when taking into consideration average results. However, some clinically relevant differences were identified per patient, which require further attention and improvement in future treatment plans